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In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process.

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We present novel methods for estimating spontaneously expressed emotions in speech. Three continuous-valued emotion primitives are used to describe emotions, namely valence, activation, and dominance. For the estimation of these primitives, Support Vector Machines (SVMs) are used in their application for regression (Support Vector Regression, SVR). Feature selection and parameter optimization are studied. The data was recorded from 47 speakers in a German talk-show on TV. The results were compared to a rule-based Fuzzy Logic classifier and a Fuzzy k-Nearest Neighbor classifier. SVR was found to give the best results and to be suited well for emotion estimation yielding small classification errors and high correlation between estimates and reference.

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High-resolution OH 1612-MHz spectra are presented of the supergiant OH-IR sources VY CMa, VX Sgr, IRC 10420 and NML Cyg. The spectra have a resolution of 300 Hz. Narrow components in the spectra have linewidths as small as 550 Hz (0.1 km s-1) but there is no evidence for components narrower than this. These results are in accord with present understanding of maser line-narrowing and of the physical conditions in the OH maser regions. Many of the narrow components have an appreciable degree of circular polarization which is not apparent at the lower frequency resolutions usually employed. The circular polarization indicates the presence of magnetic fields of approximately 1 mG in the circumstellar envelopes, at distances of approximately 3x10(16) cm from the central stars. These fields are strong enough to influence the outflow from the stars, and may help to explain some of the asymmetries which are seen in their circumstellar envelopes.